The low strain test method has become the prevalent method for integrity testing of cast in situ foundation piles. The automated interpretation of the sonic echo traces resulting from this test would prove beneficial to industry through the standardisation of the test method procedure and a reduction in the time spent analysing results. Therefore, in this research the generalisation and feature extraction strengths of artificial neural networks have been exploited to aid test trace interpretation. This study involved the identification of three multilayer networks considered most suitable for the heteroassociative function approximation task described above. Multilayer Perceptron (MLP) networks, Radial Basis Neural Networks (RBNN) and Wavelet Basis Neural Networks (WBNN) have all been trained using numerically generated data and their performances compared to identify the optimum network type. While each network presented similar strengths and weaknesses in fault diagnosis, statistical analysis suggested that the MLP network was marginally more successful in quantifying changes in cross-sections along the pile length. Field data from three test sites have confirmed that the network can identify, locate and quantify significant (±13%) changes in diameter along the pile length (within known test method limitations). The network has also diagnosed changes in diameter at the pile head. This task is notoriously difficult using conventional techniques and has been facilitated through the development of a novel pre-processing technique: the wavelet mobility scalogram.